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在没有真实对照的情况下评估海马体重演。

Evaluating hippocampal replay without a ground truth.

作者信息

Takigawa Masahiro, Huelin Gorriz Marta, Tirole Margot, Bendor Daniel

机构信息

Institute of Behavioural Neuroscience (IBN), University College London (UCL), London, United Kingdom.

出版信息

Elife. 2024 Nov 28;13:e85635. doi: 10.7554/eLife.85635.

Abstract

During rest and sleep, memory traces replay in the brain. The dialogue between brain regions during replay is thought to stabilize labile memory traces for long-term storage. However, because replay is an internally driven, spontaneous phenomenon, it does not have a ground truth - an external reference that can validate whether a memory has truly been replayed. Instead, replay detection is based on the similarity between the sequential neural activity comprising the replay event and the corresponding template of neural activity generated during active locomotion. If the statistical likelihood of observing such a match by chance is sufficiently low, the candidate replay event is inferred to be replaying that specific memory. However, without the ability to evaluate whether replay detection methods are successfully detecting true events and correctly rejecting non-events, the evaluation and comparison of different replay methods is challenging. To circumvent this problem, we present a new framework for evaluating replay, tested using hippocampal neural recordings from rats exploring two novel linear tracks. Using this two-track paradigm, our framework selects replay events based on their temporal fidelity (sequence-based detection), and evaluates the detection performance using each event's track discriminability, where sequenceless decoding across both tracks is used to quantify whether the track replaying is also the most likely track being reactivated.

摘要

在休息和睡眠期间,记忆痕迹会在大脑中重演。人们认为,重演期间大脑区域之间的对话有助于稳定不稳定的记忆痕迹以便长期存储。然而,由于重演是一种内在驱动的自发现象,它没有一个基本事实——一个可以验证记忆是否真正被重演的外部参考。相反,重演检测是基于构成重演事件的顺序神经活动与主动运动期间产生的相应神经活动模板之间的相似性。如果偶然观察到这种匹配的统计可能性足够低,则推断候选重演事件正在重演该特定记忆。然而,由于无法评估重演检测方法是否成功检测到真实事件并正确排除非事件,不同重演方法的评估和比较具有挑战性。为了解决这个问题,我们提出了一个评估重演的新框架,并使用来自探索两条新颖线性轨迹的大鼠的海马神经记录进行了测试。使用这种双轨迹范式,我们的框架根据其时间保真度(基于序列的检测)选择重演事件,并使用每个事件的轨迹可辨别性评估检测性能,其中跨两条轨迹的无序列解码用于量化正在重演的轨迹是否也是最有可能被重新激活的轨迹。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3280/11666237/537f7841803a/elife-85635-fig1.jpg

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